Benczúr, András

Frigó, Erzsébet

Kelen, Domokos

Kocsis, Levente

Pálovics, Róbert

SZTAKI Publication Repository

Alpenglow: Open Source Recommender Framework withTime-aware Learning and Evaluation∗Erzsébet Frigó Róbert Pálovics Domokos Kelen Levente Kocsis András A. BenczúrInstitute for Computer Science and ControlHungarian Academy of Sciences (MTA SZTAKI){frigo.erzsebet, rpalovics, kdomokos, kocsis, benczur}@sztaki.huABSTRACTAlpenglow1 is a free and open source C++ framework with easy-to-use Python API. Alpenglow is capable of training and evaluatingindustry standard recommendation algorithms including variantsof popularity, nearest neighbor, and factorization models.Traditional recommender algorithms may periodically rebuildtheir models, but they cannot adjust online to quick changes intrends. Besides batch training and evaluation, Alpenglow supportsonline training of recommendation models capable of adapting toconcept drift in non-stationary environments.1 INTRODUCTIONAvailable free and open source recommender systems2 mostly fol-low the needs of static research data such as the Netflix prize compe-tition with a predefined subset of the data for training and anotherfor evaluation. In a real service, users request one or a few recom-mendations at a time and get exposed to new information that maychange their preferences for their next visit. Furthermore, recom-mendation applications usually require top-k list recommendationsand provide implicit feedback for training recommender models [1].In a real application, top item recommendation by online learningis hence more relevant than batch rating prediction. We target top-k recommendation in highly non-stationary environments withimplicit feedback [1, 2, 4]. Our goal is to promptly update the rec-ommender models after each user interaction by online learning.We present Alpenglow, a conjoint batch and online learningrecommender framework. When Alpenglow reads a stream of user–item interaction events, first it constructs a recommendation toplist for the user. Next, the consumed item is revealed, the relevanceof the top list is assessed, and the model is immediately updated.Alpenglow works in a single server shared memory multithreadedarchitecture.2 ALPENGLOW IMPLEMENTATIONAlpenglow is capable of training various factorization, similarity,recency, and popularity based models including• temporal popularity and item-to-item models;• time sensitive variants of nearest neighbor, e.g. with the intro-duction of a time-decay;• batch and online matrix factorization (MF), including asymmetricMF, SVD++ and other MF variants;• time-aware online combination [2] of all these models.∗Support from the EUH2020 grant Streamline No 688191 and the “Big Data—Momentum”grant of the Hungarian Academy of Sciences.1https://github.com/rpalovics/Alpenglow2https://github.com/grahamjenson/list_of_recommender_systemsAlgorithm 1: An Alpenglow experiment in Python.import alpenglowfrom alpenglow.experiments import BatchAndOnlineExperimentimport pandasdata = pandas.read_csv("/path/to/sample_dataset")factor_model_experiment = BatchAndOnlineExperiment(top_k=100,dimension=10,online_learning_rate=0.2,batch_learning_rate=0.07,number_of_iterations=9,period_length=608400 # 1 week in sec)rankings = factor_model_experiment.run(data, verbose=True)results = alpenglow.DcgScore(rankings)The framework is composed of a large number of componentswritten in C++ and a thin Python API for combining them intoreusable experiments. It is compatible with popular packages suchas the Jupyter Notebook, and is able to process data from Pandasdata frames. Furthermore, the framework provides a scikit-learnstyle API as well, for traditional batch training and evaluation.The Python API is illustrated in Algorithm 1. In the code sample,user–item pairs are read into a data frame. Then we set up anexperiment, in which 10-dimensional factor models are periodicallybatch trained and then continuously updated by online learning.The learning rates, the batch iterations, the batch training periodsand possibly negative and past event sample counts and otherparameters are passed to the object. When running the experiment,we obtain the list of rankings, which is finally evaluated by onlineDCG (see Section 3). Both rankings and online DCG scores arestored in data frames. Ongoing work includes further modularizingthe components of mixed batch and online models.Another advantage of the Python API is that it gives access to themodular construction of the C++ core implementation. Users mayconstruct recommenders from various models, objectives, updaters,learners and run their experiments in different experimental settings.After a new record is evaluated, the training process is orchestratedby learners, which execute batch, online or sampling learning strate-gies. Updaters train themodels by altering their states, which in turnuse the trained states to provide predictions to be evaluated. Theupdaters are often defined using modular objectives. The frameworkalso provides a number of preconfigured experiments ready to berun on a given data set. For evaluation, both rating and rankingbased measures are available in an online evaluation frameworktimei2ii4ui1Figure 1: Temporal evaluation and learning in Alpenglow.includingMSE, DCG, recall, or precision, which are all continuouslyupdated.3 TEMPORAL EVALUATIONIn an online setting as in Fig. 1, whenever a new user-item inter-action is observed, we assume that the user becomes active andrequests a recommendation. Hence for every single unique event,Alpenglow executes the following steps:(1) generates top-k recommendation for the active user,(2) evaluates the list against the single relevant item that the userinteracted with,(3) updates its model on the revealed user-item interaction.We use DCG computed individually for each event and averaged intime as an appropriate measure for real-time recommender eval-uation [2]. If i is the next consumed item by the user, the onlineDCG@K is defined as the following function of the rank of i re-turned by the recommender system,DCG@K(i ) =0 if rank(i ) > K ;1log2 (rank(i ) + 1)otherwise.4 ONLINE LEARNINGOnline algorithms read the data in temporal order and may processeach record only once. For example, the online variant of a matrixfactorization model with gradient descent updates the correspond-ing user and item latent vectors after each observed interaction.Compared to batch recommenders, online models may be advanta-geous, as they• can adopt to temporal effects, hence may handle concept drift,• can often be trained significantly faster than their batch variant.Next we present an experiment of the simplest batch and onlinetrained Alpenglow models. We use data carefully distilled from the(user, artist, timestamp) tuples crawled from Last.fm [3].• We deleted artists appearing less than 10 times.• For each user-artist pair, we kept only the first occurrence anddeleted all others so that we only recommend new artists.• We discarded playlist effects: to avoid learning automaticallygenerated sequences of items, we kept only those user-item in-teractions that start a user session.The final data contains 1,500-2,000 events per day for over one year.Figure 2 shows the best performing 10-dimensional factor modelsby batch and online training. All of them are trained with stochasticgradient descent (SGD) for mean squared error (MSE) on implicitdata. The batch model is only retrained weekly, with several it-erations of lower learning rate lr = 0.07. In contrast, the onlinemodel uses a single iteration and processes each record only once,0 10 20 30 40 50time (weeks)0.000.010.020.030.040.05average weekly DCGbatchonlinebatch & onlineFigure 2: Performance of the batch, online and batch & on-line methods over the Last.fm data set. DCG scores are com-puted individually for each unique user-artist interactionand then averaged weekly.immediately after it is observed, and applies higher learning ratelr = 0.2. We evaluated top-k recommendation for each single in-teraction by using DCG and then computed weekly averages. Asseen in Figure 2, the performance of the online model is close tothe batch model, despite the fact that it cannot iterate in the data.Finally, we describe the batch&online model, which is imple-mented in Algorithm 1. In this model, we periodically re-train themodel at the end of each week by batch SGD. Afterwards, we trainthe model during the next week via lightweight online updates.Batch&online results in significant improvement over both individ-ual models.5 CONCLUSIONS AND FUTUREWORKWe presented Alpenglow, a C++ recommender framework withPython API. The current version of the code is able to producerecommendermodels that can adapt to non-stationary effects in realrecommendation scenarios. It includes batch and online variants ofseveral standard recommendation models.The goal of Alpenglow is twofold. First, it produces temporalrecommendation models that can be combined to batch models toachieve significant performance gains. Second, the framework cansimulate the streaming recommendation scenario offline. Henceit supports the selection and hyperparameter tuning of modelstrained on streaming data.In our future work, we intend to connect Alpenglow with otherpopular Python packages. Furthermore, our plan is to advance thearchitecture of the framework towards distributed recommenderAPIs (Apache Flink, Apache Spark) and data streams, thus mak-ing it possible to rapidly prototype and evaluate online learningrecommenders.REFERENCES[1] X. Amatriain and J. Basilico. Past, present, and future of recommender systems:An industry perspective. In Proceedings of the 10th ACM RecSys, 2016.[2] R. Pálovics, A. A. Benczúr, L. Kocsis, T. Kiss, and E. Frigó. Exploiting temporalinfluence in online recommendation. In Proceedings of the 8th ACM RecSys, 2014.[3] R. Turrin, M. Quadrana, A. Condorelli, R. Pagano, and P. Cremonesi. 30musiclistening and playlists dataset. In RecSys Posters, 2015.[4] J. Vinagre, A. M. Jorge, and J. Gama. Evaluation of recommender systems instreaming environments. InWorkshop on Recommender Systems Evaluation, October10, 2014, Silicon Valley, United States, 2014.

Alpenglow: Open source recommender framework with time-Aware learning and evaluation

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